BACKGROUND: AVP (arginine vasopressin) shows unique hemodynamic characteristics, as a vasopressor. AVP has been tried in many cathecholamine refractory vasodilatory situations, and sometimes resulted in effective hemodynamic improvement. In this study, we hypothesized that low dose AVP infusion could recover the decreased SVR (systemic vascular resistance) induced by milrinone infusion with minimal effect on PVR (pulmonary vascular resistance). METHODS: Sixteen patients undergoing OPCAB participated in this study. After a loading dose milrinone was infused, low dose vasopressin infusion was started and titrated until the systemic blood pressure increased by 20%. During the study, hemodynamic factors including pulmonary capillary wedge pressure and cardiac output were measured using a continuous thermodilution technique with a Swan-Ganz catheter. RESULTS: Milrinone infusion reduced both SVR and PVR. And vasopression infusion increased SVR, but show relatively less effect on PVR. CONCLUSIONS: Low-dose vasopressin infusion could be used to recover the SVR decrease caused by milirinone infusion with little effect on PVR.
Blood Pressure ; Cardiac Output ; Catheters ; Hemodynamics* ; Humans ; Milrinone* ; Pulmonary Wedge Pressure ; Thermodilution ; Vasopressins*

OBJECTIVE: This study aimed to determine whether simultaneous decreases in the serum levels of cell adhesion molecules (intracellular cell adhesion molecule-1 [ICAM-1], vascular cell adhesion molecule-1 [VCAM-1], and E-selectin) and S100 proteins within the first 24 hours after the return of spontaneous circulation were associated with good neurological outcomes in cardiac arrest survivors. METHODS: This retrospective observational study was based on prospectively collected data from a single emergency intensive care unit (ICU). Twenty-nine out-of-hospital cardiac arrest survivors who were admitted to the ICU for post-resuscitation care were enrolled. Blood samples were collected at 0 and 24 hours after ICU admission. According to the 6-month cerebral performance category (CPC) scale, the patients were divided into good (CPC 1 and 2, n=12) and poor (CPC 3 to 5, n=17) outcome groups. RESULTS: No difference was observed between the two groups in terms of the serum levels of ICAM-1, VCAM-1, E-selectin, and S100 at 0 and 24 hours. A simultaneous decrease in the serum levels of VCAM-1 and S100 as well as E-selectin and S100 was associated with good neurological outcomes. When other variables were adjusted, a simultaneous decrease in the serum levels of VCAM-1 and S100 was independently associated with good neurological outcomes (odds ratio, 9.285; 95% confidence interval, 1.073 to 80.318; P=0.043). CONCLUSION: A simultaneous decrease in the serum levels of soluble VCAM-1 and S100 within the first 24 hours after the return of spontaneous circulation was associated with a good neurological outcome in out-of-hospital cardiac arrest survivors.
Blood-Brain Barrier ; Cardiopulmonary Resuscitation ; Cell Adhesion ; Cell Adhesion Molecules ; E-Selectin ; Emergencies ; Endothelium ; Heart Arrest* ; Humans ; Intensive Care Units ; Intercellular Adhesion Molecule-1 ; Observational Study ; Out-of-Hospital Cardiac Arrest ; Prospective Studies ; Retrospective Studies ; S100 Proteins ; Survivors* ; Vascular Cell Adhesion Molecule-1*

BACKGROUND: Non-invasive prenatal testing (NIPT) using cell-free fetal DNA from maternal plasma for fetal aneuploidy identification is expanding worldwide. The objective of this study was to evaluate the clinical utility of NIPT for the detection of trisomies 21, 18, and 13 of high-risk fetus in a large Korean population. METHODS: This study was performed retrospectively, using stored maternal plasma from 1,055 pregnant women with singleton pregnancies who underwent invasive prenatal diagnosis because of a high-risk indication for chromosomal abnormalities. The NIPT results were confirmed by karyotype analysis. RESULTS: Among 1,055 cases, 108 cases of fetal aneuploidy, including trisomy 21 (n = 57), trisomy 18 (n = 42), and trisomy 13 (n = 9), were identified by NIPT. In this study, NIPT showed 100% sensitivity and 99.9% specificity for trisomy 21, and 92.9% sensitivity and 100% specificity for trisomy 18, and 100% sensitivity and 99.9% specificity for trisomy 13. The overall positive predictive value (PPV) was 98.1%. PPVs for trisomies 21, 18, and 13 ranged from 90.0% to 100%. CONCLUSION: This study demonstrates that our NIPT technology is reliable and accurate when applied to maternal DNA samples collected from pregnant women. Further large prospective studies are needed to adequately assess the performance of NIPT.
Aneuploidy ; Chromosome Aberrations ; DNA ; Down Syndrome ; Female ; Fetus ; High-Throughput Nucleotide Sequencing ; Humans ; Karyotype ; Plasma ; Pregnancy ; Pregnant Women ; Prenatal Diagnosis ; Prospective Studies ; Retrospective Studies ; Sensitivity and Specificity ; Trisomy

Background: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. Methods: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. Results: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI],0.9352–0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612– 0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860–1.0000); sensitivity, 0.9594 (95% CI, 0.9245–0.9943); specificity, 0.9714 (95% CI, 0.9162–1.0000); PPV, 0.9916 (95% CI, 0.9752–1.0000); NPV, 0.8718 (95% CI, 0.7669–0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825–0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845–0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087–0.9867); sensitivity, 0.9595 (95% CI, 0.9145–1.0000); specificity, 0.6500 (95% CI, 0.5022–0.7978); PPV, 0.8353 (95% CI, 0.7564–0.9142); NPV, 0.8966 (95% CI, 0.7857–1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. Conclusion We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Background: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. Methods: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. Results: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI],0.9352–0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612– 0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860–1.0000); sensitivity, 0.9594 (95% CI, 0.9245–0.9943); specificity, 0.9714 (95% CI, 0.9162–1.0000); PPV, 0.9916 (95% CI, 0.9752–1.0000); NPV, 0.8718 (95% CI, 0.7669–0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825–0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845–0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087–0.9867); sensitivity, 0.9595 (95% CI, 0.9145–1.0000); specificity, 0.6500 (95% CI, 0.5022–0.7978); PPV, 0.8353 (95% CI, 0.7564–0.9142); NPV, 0.8966 (95% CI, 0.7857–1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. Conclusion We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Objective: We aimed to compare the multi-marker strategy (copeptin and high-sensitivity cardiac troponin I [hs-cTnI]) with serial hs-cTnI measurements to rule out acute myocardial infarction (AMI) in patients with chest pain. Methods: This prospective observational study was performed in a single emergency department. To test the non-inferiority margin of 4% in terms of negative predictive value (NPV) between the multi-marker strategy (0 hour) and serial hs-cTnI measurements (0 and 2 hours), 262 participants were required. Samples for copeptin and hs-cTnI assays were collected at presentation (0 hour) and after 2 hours. The measured biomarkers were considered abnormal when hs-cTnI was >26.2 ng/L and when copeptin was >10 pmol/L. Results: AMI was diagnosed in 28 patients (10.7%). The NPV of the multi-marker strategy was 100% (160/160; 95% confidence interval [CI], 97.7% to 100%), which was not inferior to that of serial hs-cTnI measurements (201/201; 100%; 95% CI, 98.2% to 100%). The sensitivity, specificity, and positive predictive value of the multi-marker strategy were 100% (95% CI, 87.7% to 100%), 68.1% (95% CI, 61.7% to 74.0%), and 27.2% (95% CI, 18.9% to 36.8%), respectively. The sensitivity, specificity, and positive predictive value of serial hs-cTnI measurements were 100% (95% CI, 87.7% to 100%), 85.5% (95% CI, 80.4% to 89.8%), and 45.2% (95% CI, 32.5% to 58.3%), respectively. Conclusion The multi-marker strategy (copeptin and hs-cTnI measurement) was not inferior to serial hs-cTnI measurements in terms of NPV for AMI diagnosis, with a sensitivity and NPV of 100%. Copeptin may help in the early rule-out of AMI in patients with chest pain.